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WarbyParker.com-slash-covered. Welcome to the MrBeacon podcast. This week we're delving into the world of artificial intelligence and machine vision with the CEO and co-founder of Always AI. Marty Bidd is an incredible executive. He has seen some amazing things in high-tech history. He was vice president of Oracle Online. President of Cybase 365, Chairman and CEO of his own live-ops company, Chief Executive Officer of BlackBerry.
And he's now a co-founder and CEO of Always AI. You don't have to go far to read and hear about how artificial intelligence is changing everything. And I've always wondered how it works and what it can be used for outside of chat GPT. And Marty has some amazing examples of how
this is being used in stores, in restaurants, in mines. And I think it'll really peak your interest, hopefully stimulate some ideas and give you a bit more confidence in knowing this particular part of what's happening in the world of digital to physical convergence. The MrBeacon Ambient IoT podcast is sponsored by Williot, bringing intelligence to every single thing. So Marty, welcome to the podcast. Yeah, thank you. So I'd love it if you can start off just by introducing us to your company,
you're the co-founder and CEO of Always AI. What does your company do? Yeah, we're a vision AI company. So what does that mean? We have a software platform that allows companies to build computer vision applications. And there's a lot involved in that process, but build computer vision applications. And they'd actually get them out on the edge running. So they can collect data from cameras that are out in the real world,
that'll help them run their business. So where the software platform that gives you an end-end capability to build computer vision apps, run those apps 24-7 out in the real world, and then get all that data that people are really after to help them make better business decisions. That's Always AI. And are you building a platform that other people will develop on, or would you go to general motors and kind of give them a turnkey solution? Yeah, we do both. So it's a great
question. So in some cases, the customer, they have IT departments, they have developers, and they're going to basically use our platform to create those applications themselves and get a running out on their cameras. So everything the platform enables. That's great. And it's built for them to just go crazy prototype, build as many apps as you want, get them out on those many cameras as you want. Other cases, a lot of cases actually, the customer will say, hey,
I don't really have a whole bunch of folks, but I definitely need to do this. Can you guys actually provide the solution? And then in that case, my engineering team will work with the business people, and we'll build the AI model, we'll train the model, we'll develop the application to their specs, we'll actually get it out running on the cameras and so forth. So it's, it's, but
and it seems like you probably have one of the classic entrepreneurial problems of you. You you probably have some great technology, but it can be used for a million different things. Right. What have you decided to focus on as the key use cases? Yeah, no, you're right. You're right. It will turn out that there's a couple of industries or verticals that are, are early adopters. Right. So one is the industrial space and specifically mining and manufacturing.
Turn out to be early adopters of computers and you probably wouldn't have picked that if you were building a, you know, business from scratch, but it was like, oh, I get it. You know, if you think about mining, it's it's under a lot of pressure because you know, we need to get more and more of these metals out of the earth. Why? Because everybody is buying electric vehicles and we're all using iPhones and, you know, laptops and so forth and all of that relies on,
on metals and, but they need to run those minds more efficiently. So cameras can show them where they're being inefficient or where they're having issues or problems and maybe labor constraints or or what have you also health and safety. Same thing with manufacturing. We work, for example, with a lumber made manufacturer and and they want to understand how efficient are they cutting lumber and how safely are they cutting lumber and are they getting the kind of output that they
need to get? Are they catching anomalies or mistakes before they become a real problem? So industrial is one space that we really folks on a lot. The other one, almost 180 from that is retail. So retail is obsessed with what? With customers. How many customers do I have? Where are they going in my store? What brands are they? Are they most focused on? Right. So there's all the customer analytics for a retailer but also behind the kid what's called behind the counter. So for example, if you're
a restaurant, how efficiently am I making stuff? Like it? Am I quote, manufacturing a sandwich or it pizza as quickly and efficiently as I can? So these are two areas that are really hot. It's all about operational improvement. It's all about getting real time data like real time insight so you can act on what's actually actually happening. And you can catch problems before they become big issues. And this is where computer vision really thrives. So how would your product help
subway monitor the sandwich making process in a way that can be actioned and acted up? Yeah. So we do things like we can actually assess what's called the average traversal time from the very beginning of that process of making a sandwich to the very end. How long did that take on average? And if you know that number, then you can benchmark different subway stores against each other. You can say, okay, this store right here does that in two and a half minutes and they have really good
results and they sell a lot of sandwich. This one over here seems they're in the five and a half minute mark. That's twice the other guy. Why? Like what's what's going on? Right? There's something going on there. Then they can diagnose it which they literally do by zone. So one zone might be bread and the other zone is condiments and the other zone is meats. So they can start to drill down into, oh, we're really starting to have a problem right here in this one section. Let's fix it.
So that's a simple example, but that's a real example. So before that, it was human beings trying to figure it out or you might look at weekly or monthly data and try to understand what's going on. Now you're just literally getting a real time on a dashboard and you can see the dashboard and you can make decisions based off that real time. I think as an employee, I would have mixed
feelings about this on one hand. I'd feel like, oh, this is good because this AI is impartial and dispassionate and just because, you know, I've got my hair partying, well, my hair partying is a complete mess, but it's going to judge me objectively. And then the flip side is the obvious big brother thing. I'm sure you have had to deal with that. What's the, and I can't imagine that unions love this, but I guess probably a lot of fast food shop. Very few are unionized,
so maybe that's not so much of an issue. But yeah, but construction sites and mines and others are. Now it's a real, it's a great question. Yeah, and it's an issue that needs to be addressed. So on the privacy side, let's start with that. Yeah, you can't identify somebody by name, right? You can't, you can't do that. In fact, in every situation that I've mentioned and others that
I can tell you about, you have to blur faces, right? So you literally as part of the computer vision application, you're not, you're blurring the actual identity of a person working there. You need to protect their personal identity. But the camera is identifying, okay, that's a specific individual and they're here in this zone and so forth. So there is that level of detail. But that's, it's an important issue and the company needs to be upfront about how the data is being
used and so forth. Now on the flip side of that, which is what is interesting and not expected, is that what companies have started to do is there's upside in the sense of if you use this tool accurately, there's bonuses, there's, you're almost like gamifying the process, you're kind of like, hey, store A, you guys can make a little bit more money than store B because you're performing
better, right? So then there's this awareness that like, okay, I can use that tool to actually potentially improve the performance of the store and maybe that filters down to everybody that's involved in that. But these are real issues that the industry is working out. We worked with Dixon work with Burger King in Europe, which has very strong rules and regulations around
personal identity and so forth. And, and Burger King wanted to know exactly how long it took from the time somebody entered the door, they ordered their meal, they picked up the bag and they left. So again, that amount of time. But we had to blur like literally everybody's face and the person is identified just by number. There's no, you have no idea that's marty beard or Steve or
you don't know that it's just, it's just a number. But these are real issues and these, these are issues that I think AI and general is dealing with and then computer vision specifically is going out to do. Can you give feedback to an individual employee? You know, there's a, there's an AI company that kind of monitors sales calls and tells people, oh, you're talking too much and try using, you know, the best performing people use this word. Can you do the same for sandwich
production or wood production? Can you be a coach? Well, in the, in the case of by for example, mining, if, if you, if somebody is, is being unsafe, right? Which is a real, I mean, that's, that's, that's a real issue. This is not like somebody that's going to stub their toe, right? This is like something bad may happen. Then, yeah, you absolutely want to take action on
that particular person. And you would know that particular person because the camera is saying, there's a person that's not roked in appropriately in that area right there and you could, you go take care of it. Or in the restaurant example I gave earlier, let's say the dashboard is showing a manager that we're really having backup in the bread section. Like for some reason, we're having backup in the bread section. You're going to go to the bread section and talk to the person there and say,
what's going on? It may be, oh, I don't have enough bread. Oh, okay. You know, let me, let me help you out. So yeah, you can, you can definitely take, take action in real time based on yours. And can you talk a bit more about the retail applications? That tends to be a vertical, you know, we cover IoT digital physical convergence. But for some reason, I think it's basically just, I'm super interested in retail. But what are the other retail applications? We have a,
we do work with lazy boy furniture outlets. You may have heard of those. And, you know, that's called specialty retail. So we were just talking about restaurants. But now we're into so-called specialty retail furniture stores, hardware stores, you know, things like that. They have a very simple need, which is who's coming in, how many people are coming in, where are they going in the store? Am I, am I assigning the right salesperson to that person that
came in? So you would say, oh, good, good to human being, did that. Some of these stores are enormous. Right. And so it's, they want to know, okay, that person came in. They're very interested in, in couches. The right salesperson is, is Dave. Let's get Dave over there to go with me with that person, et cetera. And so it's just a little bit more real time kind of ability to mix and match appropriately human beings, you know, the customers of the right salesperson.
We're in about 80 of those stores now. And, and also obviously they're understanding who's coming in, how many people are coming in, demographics of the people coming in. They're starting to understand a lot more about their customer base and their prospect base. Fascinating. So that's a simple, simple application, but it's sophisticated computer vision. These are deep learning AI models that are deployed out in the facility. And, and, you know,
there's a lot of, a lot of technology that goes behind that. And how are you delivering the prompts, the nudges to the staff to look after someone who's been roaming around in the mattress area? Yeah, they have a dashboard that they're that they're using. The manager can literally see color coded, you know, all that information about how many and where and zones and so forth is kind of captured in a nice, in a nice dashboard that is put together by our partner, which is called a company called
Trackwell. So we partner with Trackwell, which is really focused on that analytics presentation. We're all the AI magic behind that. It's kind of feeding feeding that and, uh, yeah. So it's a dashboard. Just think of an iPad where somebody has it and they can see what's going on. And with retail, there's obviously all this technology there, but also the buyers are famously unimpressed by technology for technology's sake. How do you sell to a company like that?
Yeah. Um, it's going to be a, you're absolutely right. In fact, the buyers that would apply to all industries. Yeah. It's, you know, but retail, yeah, very tight margins and, and, uh, don't want anything that gets in the way of efficiency and meeting those margins. Yeah, you need to demonstrate, demo here. This is what it does. And this is how you will get ROI from this application. Um, I brought, I guess it's ironic. If you're running a computer vision company, you better
visually show people how it's going to help them. You can't hold up a PowerPoint and say, I would like to talk to you about vision, right? So you need to show people that humans are very visual. And, uh, I think they get it, you know, when they see it, they kind of get it. Um, we'll do very simple, percept concept for people where you just put up one camera. By the way, and I should
even mention, in most cases, they're already epic cameras. Yeah. Yeah. You just, you can, you're already using it, you know, those black, you know, you see the camera up in the ceiling. It's probably just doing simple security footage. Um, but you can get to that camera, um, through always AI. And then you could do much more sophisticated computer vision off that camera. So you just quickly set it up and showcase counting people zone analysis. You know, it's easy to set up a few
zones and, um, and go from there. And then I think, you know, when people kind of go, oh, I get it. Okay. This, all right. Can you do this? Can you do that? And now you're getting into the more sophisticated needs. And so what was your approach to getting that deal? Did you say, look, we think there's an opportunity in, uh, specialty retail. And we're going to find this partner. And we're going to create a demo. And then we're going to go and call on all the specialty retailers.
And so that was, you got it. That's well put. Yeah. That's, I mean, literally, um, our partner track well, um, uh, already had some experience with lazy boy. And, uh, some opportunity to provide them more sophisticated AI. Uh, they know us. And so they said, hey, we want to, let's go in, we demoed. And exactly, you just laid it out. Exactly. It was kind of like, okay, that's what we need. And then we worked and we built it. And then we started in a couple of early stores,
optimized the application. You know, AI is very dynamic, right? It's, it's garbage and garbage out. It's all based on the model, right? So you want a model that's really optimized for this specific news case. And that optimization process takes time. Right. You need to get out of the real world and you need to collect that data and you need to look at it and try it and then refine it and, and so forth. So we did that in the early stores.
And now we're in a very, to chink, to chink, to chink, to chink, just roll it out, uh, store by store. And can this technology be used to, uh, combat shrink? That seems to be one of the big issues in retail. Can you spot people that are out to steal things? Yeah, you, you could, um, you could also shrinkage also could be, for example, um, like in the case of, uh, you know, fast casual restaurant like a medisato barbs or, uh, you know, or a quick service restaurant like McDonald's
or so forth. They're just, but not equal about not wasting food or not wasting things. So wastage is kind of similar. It's like, let's not waste food, right? So cameras are fantastic. That's right. You can really analyze, yeah, we're wasting here and not here so forth. And then obviously shrinkage. That's more of a security. Um, you, you could potentially have the cameras on the checkout, self checkout, which is where a lot of that seems to happen.
You could use cameras there, but we're not a security company per se. We can help with security, but that's not really our forte. And what does the deployment look like in terms of the infrastructure you introduced your company as edge, uh, you use the word edge a couple of times. Yeah. Yeah. So I have local servers. Uh, they like, uh, with a lot of, uh, and video, uh, GPUs or yeah. So maybe in this case, let's back up from, from what happened. So I'll just take
the case of, uh, let's take some real cases, so like lazy boy. Okay, there are cameras. Right. Let's say there's four to five cameras. These are just simple. What are called IP cameras. They're, they're, they're able to hook up to the internet, right? They can accept Wi-Fi, they can, etc. Those are the cameras. They're capturing the images. Those camera feeds go to what's called an edge device, right? And a brand that is everybody knows called, you know, Nivea, uh, sells edge devices.
So do a lot of other people. I'm just using Nivea as, as an example. And they have a line of products called Jetsons. They're these boxes. They're about this big. And the camera feeds into the box. And what's the box doing the box actually has the AI model, the actual models there. And it's leveraging the GPUs, the graphical processing unit sitting in the, in the Nivea, uh, box. And it's taking the camera feeds in and it's applying the model that was built. How many people, where are
those people? Demograph this so the people, what zones are they? And whatever the model was that was built. It's the magic's happening literally on that device. And, and that's, and so again, if I'm backing up and then always AI is the platform that built the app, deployed the app, put it all on that device to actually give you that, that, that real time fee. Okay. Now in some cases, you got the cameras. And instead of going to an edge device, they're going to the cloud, right? So a company
might say, I don't want to put out an edge hardware. I just want to go directly to the cloud. That's fine. And we do, we do a lot of that. The only problem with that is it's expensive, right? Because the cloud is going to charge you for the, for the inference, right? To change, to change, to change, to change. Every time, you know, the inference is just the logic. It's just saying, is that a person,
is that a person? Is that a person? And that's to change, to change, to change, right? In, in, in the cloud on an edge device, there's no charge for that. It just runs 24 seven. There's no, there's no charge. But that, that's the generic architecture. You got cameras connected to either an edge device or the cloud. And then you've got the magic happening the AI rhythm algorithm, the mat, I call the magic is either literally happening in that edge device or in the cloud. And the magic is all
created and built by always AI in our, in our situation. And how expensive are those, and video devices? Again, going backwards, the cameras, you know, I mean, literally cameras now are like, what? 70 bucks, 100 bucks. Right. I assuming that's free. That's already there. I just think about the service. Yeah. And then the edge devices are, you know, these are 700, 800,
900 bucks, something like that. But they can feed, they can feed many, many cameras. If you want to get to a beefier box, they can do more and run faster and have more GPUs and that value into maybe a couple thousand dollars. But, you know, in the case of, of, lazy boy, they're not even using the video. They're using a different product. And in, in their case, it's an edge device. And I think it runs somewhere around 130 bucks, something like that. So it's, so the edge cost,
this is Moore's law, right? Would you and I've seen, it's like the power goes up. The cost goes down. AI magic can happen now on, on the edge. You couldn't, you know, five years ago, it was really difficult. Now you can do it. Interesting. So I remember, I'm sort of interested in your opinion on maybe an adjacent application for some technology, Amazon fresh, the, all the cameras looking at the basically streamlining payment. Yeah. Which just seems amazing. Yeah. Yeah.
And I guess I should ask you if that's something that you have worked on. But one, one of the criticisms there is hardware is really expensive because you have to have so many cameras and you can't just use the security cameras. And you've got to have like a lot of compute to do that level of processing. Is that a valid criticism or is that just it is? No, it is. It is. Yeah. I think, I think in that specific case, the way that Amazon went about it, it's amazing. But it's really,
really expensive the way that they, they did it. And if you've noticed, they've backed up that a bit and kind of shut down some of those, those stores and kind of, kind of backed off because I think the way they did it is you literally have probably hundreds of cameras in there. Now, we did it a very different way. We, we have some experience with what's called contact. Let's check out. All of us have seen this now. We all got used to like going to a target or something and then we,
we self-check out, right? It's all barcode scanning. And what's happened is now people are really looking at different modalities of contactless checkout where you don't interact with a human being. You take your items. Most people don't have a hundred items. We have 10 or less. And you put them down on something. Cameras look at it, immediately identify it. You tap your phone. You walk out. Right. RFID tags are another way that people are doing that. That's not computer vision. That's
a different technology. So I think there's a lot of effort and money going into making that process faster. The way that Amazon went about it, I think, was amazing and like, but too expensive to really roll out big time, right? And I think people are still working on this challenge. We did do a lot of work, like I said, in in contactless checkout. And interesting, difficult computer vision challenge for sure. Yeah, there's an Amazon fresh store near where we both live. I just didn't land
from you and just south of me in the color, mountain area. And it's just been sitting there. And they haven't turned it on. It's like very frustrating. I kind of want to experience this. Yeah. Where would you say this part of AI is in terms of crossing the chasm? Are you? Yeah, I think I think it's in the in the middle of that process. It's it's it's it's it's kind of a couple of you know, maybe the way to answer it is it's very different from for example,
Generative AI and kind of open AI open AI right? So that's all this have browsers. We we boot up our browser. We go to you know chat GPT and we interface with it and we have that kind of magic. But that's that's looking backwards. It's historical. So it's grabbing data that already exists out in the world on the internet and pulling it together for you in this amazing human like manner, right? And so it's almost like Wikipedia on steroids. It's kind of like I'm just going to pull
together information from you or I'm going to generate information for you. But the way I'm doing that is I'm looking it's a mirror on the past, right? I'm just grabbing information that already exists and I'm going to present it to you. Okay, when you get into computer vision, this is literally real time. New data that does not exist. Right. So this is what's happening now. This is not that chat GPT which goes up to November of 2022 or whatever the cutoff date on the on the model is.
So now you're dealing with kind of real time information and you also have hardware right? So you've got physics. You have an atoms or on you know, you've got metal devices cameras. You've got drones. You've got cars. You have things that are literally out there that are also you know, I have to be enabled to to take this AI and make it work for you to get that real time data. So my point being it's it's more complex and the industry is working its way through the best ways to do that. The
most optimal ways to do that. The cheapest way is to do that, right? And so we're we're all versus you know, open AI which I can jealously look at is yeah, I didn't have to worry about that because everybody has a browser on their laptop, their iPad, their phone, their already and it's literally just accessing magic through that. This is you got to get it running on a physical device and then from there you need to collect that information and then present it. Right. So it's a little
bit it's a little bit tougher. So I didn't get a long answer to your question but I think we're in the middle of that process. So this is an area that Elon Musk is famously very keen on. So kind of just sending all these extra sensors for the cars and just focusing on the on the camera input. Okay. And one of the things that he uses to kind of bolster the perceived value of Tesla is that the corpus of training data they have they're gathering more training data than anyone else.
Yeah. That's something well first of all do you buy that argument and does that apply to always AI do you see you building a kind of a barrier to entry because you just have more hours of video streaming into your system. No, well, so first question I do I do buy his argument because the more data you have the more training you can do the better the model will react
and therefore be able to predict and that's just an ongoing thing. In his case in the case of like OpenAI they're dealing with massive data sets like you know I think I read it something like more molecules in the you know more than molecules in the ocean. It's kind of like just massive massive massive data sets and yeah the more data the better the predictions will
will be right. In our case in in computer vision the models are much smaller right so just physically smaller they don't take as much room and as needed on those situations that's that's so that's a really important distinction and why is that because you can you can train a computer vision model offer relatively small set to do exactly what you needed to do right that's one of the reasons it can actually get on a physical device that's not a massive server sitting in a data center
somewhere it's literally on a on a smaller device. What's unique about us is our end-to-end capability. We are totally unique in that you can go all the way from collecting data to build the model train the model build your specific application literally get it out out running on a camera and get the data that whole end-to-end process you can log into always AI and do all of it by the way
you can do it all remotely. So if you're if you're on a hundred cameras out in 10 different locations you can see everything going on there just from your your desktop and your laptop so that's our uniqueness is the end-end it's not really data you know like a data model battle as much as it is actually a practical AI implementation. And so what's next for you guys from an R&D perspective
where are you taking the product? So we're at the biggest thing for us is what's called multi-modal a tech loves to come up with expressions that are like sound cool and so multi-modal all that means is another mode in in our case for example sound gets combined with vision to make the
application even better. So for example in a mining scenario I'm not only seeing what's happening I can actually hear what's happening which could be really important in a mine things are exploding and you know there's there's noises that are important I can combine that into into a synthesized AI model and that's even better even smarter and even more human-like because all of us are seeing and hearing and so forth. So that's that's so we're we just announced this that we're able to handle
multi-modal capabilities. So we've gone from vision to sound to now we're starting to deal with text and large language models and so forth which are also starting to find our way into our world. So that's super exciting because now it becomes really a AI platform not just a vision platform but again still very focused on practically getting it working. Right we're not the guys that are in the back office just doing data science work this is like we want to take that work and then
we want to get it out and get it working in the in the real world. So super exciting. Multi-modal. So multi-modal does that include text for the sorry the conversations that people are having in that way. Okay. Yeah that would. Yep you could start you could start building in voice into the model you could build in text into the model you could build in sound coming from the
sensor. So all of this is getting fed somehow by a sensor. Right. But you know it's it's obviously heading our way and we can see that in some of our earlier adopters and so forth. And what about text so I that seems like a separate thing so can you unpack that. So so text is being used for so the very beginning of this whole process of any AI is the model the model that you develop right. You know garbage and garbage out of it. I mean you want that to be as good as
you can get. And building a model is is an art form because you have to you know you collect images you annotate images you're trying to train something look for this right. There's a whole category called synthetic data which is that which is you know data that's not quote real video images from the real world it's just coming off the internet. And what's happening is you're now starting to be able to use text to describe what kind of synthetic data set you want. Please give
me 50 images of people walking in a shoe store and looking at night keys. Right. And then you get a synthetic representation of that. And then that helps you make your model better. Now you also have real images of people walking in stores and looking at night keys. But now you can also augment it with this synthetic data. It just makes everything faster and and and and kind of gets to the magic quicker. So that's super interesting because it's starting to marry the unreal world with the real
world. The synthetic with kind of the real world. Amazing. Well Marty it's been a real pleasure. We've got a whole other chat to this conversation where we delve into your amazing history. You have been part of some amazing companies. So amazing thing. Thanks very much for talking about always daily. Thank you. I'm great. Hey, I'm Ryan Reynolds. Recently I asked MintMobile's legal team
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Go to Warby Parker dot com slash covered to try five pairs of frames at home for free. Warby Parker dot com slash covered. So multi you've had an amazing career spanning Oracle, Cybase, CO, Blackberry, Confadice, CO of always AI. And I always look if someone's worked at Oracle that's like that's quite a thing in my mind because Larry Ellison runs a tight ship and they're like just legendary. How did you get to be Vice President at Oracle? What were you doing that?
I think maybe it was just I got lucky on the on the timing who knows but yeah this is really early in my career and the web is really having a big impact on e-commerce and all kinds of business applications. And Larry, Larry decided that he wanted to see how much of his sales he could do online. Right so he he was tired of having to rely on this huge sales forest you know of human beings right that that would go out and try to sell Oracle database and a legendary sales force
or legendary. Yeah, legendary. Sorry. Yeah. And at the time that I got there they were doing a lot of consulting under a gentleman named Red Lane famous executive at Oracle. And Larry was sort of wanted to get back to software sales and try to make it a more direct sale. And I was in a group that was focused on doing that first of all the media of size businesses. It was called Oracle Online. And it was all about selling database licenses online and really
leveraging the web to do that. So it was a fascinating project Larry was very involved in it. I just happened to be at the right place at the right time and they had a great opportunity to to not only work with him but just work with a wide variety of amazing talent at Oracle. And we did pretty well. We sold we sold a lot of database online. So it was kind of the start of that more online sales and that became just normal within the enterprise software space.
And how would you describe his management approach? He's like this Zellig character, the pops up whenever you watch any documentary about Zellig who value whether it's about Steve Jobs or Elon Musk or whatever. It kind of appears as a feature character. But what's he actually like when he's working on a new project like this? My very first meeting that involved him. There was a bunch of people in
a room in the background and he found out that there was a group of people in Sacra Mento. They were taking orders off fax machines and then re-keying them and he fired them all. So as I was sitting in the meeting thinking okay this is how things roll. So he found this massive inefficiency and he got upset about that and that wasn't even on the agenda. That just happened.
Right. And then I later found out I said to Zellig what happened and it was like yeah some people some people got infected not everybody but he did find something that shouldn't should have been there. The other thing I noticed about him is he so he would make decisions like that very very quickly. People would come in and spend a month on power points and put them in front of them and they'd be perfect and he would not look at them. He would just talk and just immediately get
to the key issue and immediately resolve. So very quick very focused very smart. How do you prepare to deal with someone like that? I think you go in just knowing your subject matter from every angle that you can think of. Right. And so if it was pricing you know online what are we selling the database for? How are we pricing it? You know what are the different permutations
etc. You just need to make sure that you add all that covered. And I found him like you know obviously he's legendary for his temper and all kinds of things and outside of that situation I just mentioned and really see it that much. It was more very very business focused very very get to the essence of the problem and try to figure out how to fix it. So I found it good training you know to see that. So it's been a little scary dealing with someone who just fired a bunch of
people because they were just doing the wrong thing a lot. But did you feel like did you have space to actually perform and be creative? Yeah I think so yeah and again this is me as part of a team. This is not you know Marty Deere sitting there having this one on one with Larry Ells. That was not that was not happening. But yeah I think the team look Oracle a lot of really smart people. Larry hired really really smart people from a lot of different backgrounds. Definitely a technology
oriented place. High high value of engineering backgrounds and so forth. And just a lot of people around the table that are just smart Larry was really fast. Honestly he's probably the smartest guy sitting in the room. I think everybody actually knows that not just because he owns the company
and is that multi billionaire but actually really really smart and fast. So you're on your toes you know you're subject and and then you know he like another give you another simple example is a lot of people were building a website to try to make the interface better for the customer and so forth and Larry walks into his office and boots up and asked somebody to go to amazon.com
which was relatively early then. And he said why are we redoing things? Do you think they know how to make things work where they're selling directly to customers or we're like oh yeah books yes beyond books yes why are we redoing this? That's probably a paradigm that we should follow. Just follow that. So we all like that. That makes sense. It's practical. You know they probably figured this out maybe there's some things there that are best practice that we can learn from etc.
It's just an example very practical guy. So are there things from working in that environment? I realized this is kind of one of relatively small part of your career just a few years but as you're the CEO now you're kind of in his position. Is there a set of things that you learned from him that you're putting into practice and other a set of things that you said I'm not going to do that. Yeah I think definitely a set of things from him and then after him I worked for
a gentleman named John Chen for many years. Also incredibly smart and very operationally focused and so forth. Yeah I think learning that in a meeting it's just completely focused on the business problem. Right that came across really clearly from Larry and John and really trying to understand the the business problem. Focus on that that's the most important thing. Talk get the right people around the table talk about the problem trying to resolve it and move on to the next the next
problem. So I guess you know focus and really focusing on the right issue not a lot of just smoke around the issue or things that are kind of tangential to what you're trying to try to accomplish.
I think on the other hand however and I would say especially my experience at Sybase and so forth maybe I try to also focus a little bit more on on innovation and kind of also allowing time for people to really especially we are talking about road maps and the future so it's not so much here's a business problem we need to solve it's more about where's the market going and what kind
of innovation you need to focus on. That's a very different process right and that's that's something that's really really credible to add that juice into the system so you you know you've got the new innovative technology that you need because the market just constantly moves right if you're only focused on the box of existing business problems you're going to miss the next box right so so I think you know trying to do those two things where those different hats at the
right time. Go ahead. So how did you end up transitioning to Sybase? Obviously it was a promotion so I imagine that featured in the year. I think for me you know after three or four years at Oracle I think Oracle had reached this point where it was at that time just one of the most highly valued companies in tech and and was was really one of the pixie shovels for everybody going after .com right you had that you were using an Oracle database probably on a Sun Micro Systems
machine and you were that they were just writing that that wave. It kind of felt like not tapped out but like okay that we're just going to do this forever. I ran across Sybase which was had competed with Oracle in the database space but for a whole variety of reasons had kind of gotten beat up a bit and was really looking to reinvent itself and John Chen had come in as the
CEO and was really in a in a place of like okay I'm going to do some I'm going to make some moves I'm going to do some I'm going to acquire some companies I'm going to really head into more and he needed some strategic thinking something that I always been really strong at and just wanted somebody to help him kind of make those moves and it just seemed like a such a unique opportunity that grabbed it and it turned out to be a wonderful experience. You were there for a long time what
what do you look at some of your biggest achievements? I mean we took well we transformed the company that was a you know database company that was having a hard time growing into a fast growing mobile enterprise company. So I mean it was literally pivoting from kind of a stayed you know stable but having a hard time growing database business because again Oracle was really winning at just but taking that base and in the cash that you were able to generate from that and then
move into mobile enterprise and really take it up that. Now it's just obvious like our businesses are running our mobile phones but at that time it was it was a fairly big move it was like okay I'm going to approve P.O.'s and approve you know run my HR systems and financial systems I'm able to
do that on a mobile device. So we made a huge move you know under John's leadership and and others to move into mobile enterprise we bought a lot of companies we repositioned the branding repositioned the technology capabilities repositioned the Salesforce the partner ecosystem the whole fact and what happened at the end of that is SAP bought us and for for a very high premium it was a huge payoff for for side-engineers and for the management team. So it was a fun experience.
Right amazing and that success gave the platform to move up to chairman and CEO of of line-ups. Yeah yeah yeah yeah yeah I think coming out of that experience the Syversic experience it was like all right I've worn a lot of different hats done a lot of exciting things had a big success this part of a great team and all that stuff now I want to go do it myself right I want to see
if I can be CEO of and you know I had run a large division of of Sybase it was like a $250 million business but not as CEO right I really wanted to do that and live-ups it was a super interesting company that was run by a gentleman named Maynard Webb and Maynard had come he was
pretty famous guy that had come out of eBay and he got fascinated by live-ups it was a lot of people that worked out of their houses and it was sort of labor on demand in some ways it was a precursor of like thinking of like Uber was kind of transportation on demand this was like labor on demand
and not outsourcing but actually literally on demand like I need somebody for four hours I need somebody for six hours that was super interesting and then the platform that that that live-up said Bill was used by a lot of call centers and customer service super interesting and it was
tried to leverage social and all kinds of different channels so yeah it was a fun opportunity for me to come in and and run the company and and and become chairman of the board and go through that process great VCs were best in it and yeah ran that for quite a while and so what persuaded
you to go to Blackburn where were they in their incredibly story history I mean this is a company that was just a colossus at one point I remember when I first joined my first startup I left IBM bought the company I worked at okay now's the time when I get to go from being a director
to doing a VP and running sales and everything and first thing I got from our CEO who was just an amazingly gifted guy it was was a black breed of ice and that was the iconic kind of accessory that you had when you were going to Sand Hill Road and doing all the things that you do that's right
yeah no I mean come on it's it's an iconic brand right and really had established the smartphone space but yeah when I when I got involved in that so what happened was John Chen who I mentioned earlier and had run Sybase he was asked to which would you know obviously Sybase that was a
really successful turnaround he had been asked to inject the same magic at Blackburn and they'd gone to an outsider by that I mean a non-Canadian you know it's a Canadian company Blackberry and it was like okay we're gonna bring in some Californians here and and the whole job was to pivot
from hardware only into software right hardware is really hard and smartphones you know Apple obviously had a tick butt and was quickly becoming the detector leader and you had a lot of android devices and all kinds of things going on in Blackburn is trying to fight its way in that
member it made its name on keyboards right and Steve Jobs said famous slip held his hand up and said this is a keyboard it's your fingers right and so yeah so so John had come into to to to for that turnaround and he asked me to come in as chief operating officer and kind of help him
move away from the hardcore hardware manufacturing and so forth and get into cyber security software Blackberry was always known as a very secure device the president used to the CIA used it you know it was the lockdown secure device and so I had to leverage that heritage into this cyber security
software space and so that I was involved in that whole pivot it was literally a hardcore 180 move from vastly muddy listening hardware business into a muddy making pure software company and try to do that as fast as you can so that was an interesting fascinating exhausting experience
that I went through but lucky lucky to have gone through very good so you were part of history in well more than once in your high tech history more than once in your career one what is it that you think gives you this facility with strategy what what makes a good strategist and what's
your approach you know it's I like I like thinking about patterns that that are emerging maybe from different areas and I love I love communicating about what's coming and by that I mean it's not just like a communicating like a a blog post or a speech but it's it's sort of putting into words
the ideas that people have about what's going on in the future I've always been very very comfortable doing that so I think you can take a people that might have a really deep technical background but maybe have a hard time trying to describe where that technology is going or somebody who might be
good at the description but really doesn't have a strong background in kind of the tech and how we got to where we are maybe I'm a good combination of those of those two things I've always really enjoyed thinking about what's coming and then talking about that right and and and describing that
I studied I'm a very unusual I mean I studied philosophy and classics and and in in rhetoric and and kind of in my background when I went to Berkeley and I have to tell you honestly that is far and away the most profitable business experience I've ever had by far I mean I got an MBA
that was fine I learned a lot I was probably you know one of the only MBA students that have literally never taken accounting everything seemed like everybody else already had an undergraduate business degree but yeah so I absorbed that stuff but honestly not not even remotely as useful
as some of the thinking skills and communication skills that I got coming out of Berkeley so I think that helped me as well maybe I'm just naturally predisposed that way I don't know so the philosopher and then you know the first high tech exec who is a philosophy graduate that I've
spoken to and it's really yeah my both my parents studied philosophy so I'm like I never did but I always had that respect so is it that it requires you to think very very clearly you can't just kind of waffle around the argument is that yeah right right yeah it's well first of all I think it's
it's really really hard so people people roll their eyes if you say if somebody says especially today's stem focus world somebody says what are you studying and you say I'm studying philosophy and they roll their eyes those people rolling their eyes would just flail if they it's it's very very
difficult and but also very analytical so if you can make your way through a platonic dialogue for example that is not easy but if you can do that and you can articulate it that's that shows that you can analyze a lot of different things and kind of bring it home which you think about business
you're analyzing a lot of things and trying to bring it up right so I think it yeah it gives you that that thinking discipline and you know it's it's a much much much more complicated subject than most people realize right so I think you get people coming out of there that are are very analytical
can think about super complex things and bring them home they can communicate that yeah so I'm actually surprised I haven't run across so many but I know they're out there so what's the difference between a platonic dialogue and a secratic dialogue
well they're one and the same because the main character is is Socrates right so Plato uses Socrates as his character and so the character now the Socrates is the one that's always annoying everybody he asks them questions and trying to try to arrive at some some conclusion
he's annoying he's arrogant he's he's a he's a great personality to do literature but yeah he's a character in in in Plato not a question you can tell I haven't had any questions for this podcast because they definitely we don't have a great back question but it's fascinating so I
I want to wrap this up to make sure that we have room for the rest of the discussion chapter I'm going back to strategy yeah one of the key elements you know you talk to someone to say oh we have a strategy for this strategy is a word that is bandied around very loosely
what are the key elements of the strategy in your life yeah that's like that's like I mean that's a great question and I think a strategy it's kind of like what is what is the modus operandae of the company by that I mean what is the pitch and then why are you pitching that right what is
the product that supports the pitch and okay now you're at the product level well why is that product what the customer actually needs what's the value the ROI that the customer is deriving from that product that you just pitched right so you're kind of getting through that those layers high low you're sort of starting high level like we're focused on computer vision and then you're talking about the product okay how do you actually bring that to life and then you get to the customer
why does the customer actually need that what's the business problem that's actually being being solved I think if you only if you start the other way a lot of people will just focus on the business problem the business problem but which is fine but you know again back to Steve John
to read famously said you know if I asked customers what what they want then I would never have built the iPhone or I wouldn't have built the iPad because they weren't telling me that's what they needed what they really needed was information more quickly information on the fly that was the
real name right so that he backed into you know the product and so forth so you can kind of cut both ways but yeah I think it's an all it's a word that encompasses your pitch the way that you talk about your business the product that supports that pitch and then the problem that you're
at ultimately solving were for the customer there's lots of different ways to cut it so you know the the rest of we've talked a lot about always AI but I'm interested in you know looking back on all this history and you've seen a lot of success and doubtless you've dealt with a lot of challenges
as well where are you in that Steve Jobsian not contempt but but kind of self-belief in direction where are you in listening to the customer and the kind of the lean startup and and and and and you you have an idea of this is the problem I'm going to solve this is the mountain and you know
we're going to build it and they'll they're gonna have a great success I think we're I think we're in the middle of that honestly I think it's still a vision deep learning AI and how that impacts vision let's say we can call it computer vision or vision AI is still really early it's it's a very
early that nobody can tell you exactly how it's going to evolve right and if you think about a part of the challenges human vision just our eyes and arguably the most complex system that we have in our bodies that's and and also really critical to our lives I think you know the applications
that you could build are literally what you can see right okay that's a lot that's really big so part of the challenge is it's so big and so encompassing how do you chunk it down into areas that are really worthy of more focus where a machine vision and so far it would really fit so I
think the industry is still figuring this out we're still in the middle of it some people are willing to take the leap and they're more innovative and they're kind of ahead on the curve other companies are a little more conservative they know it's coming they know it can help their
business but maybe they're not ready yet so I think we're we're we're in the middle we're in the middle of it very interesting very good well we come to the show where I ask you the hardest question it's not about aocratic dialogue it's about your three three songs that have meaning for you
and why did you be able to distill it down your songs well I can okay anything by so by anything by bot.dellar it's gonna be it's gonna be number one for me by far and I would take I would pick anything from an album called Blood on the Tracks which is a famous album but for
you know anybody under the age of 45 probably huh but uh yeah that there's a song called Tangled Up in Blue which is just an amazing song by by bot.dellar I picked that one for sure and then I'm a big Miles Davis fan so I think I probably picked something from Kind of Blue
like Silwet or you know anything like if you're on an island and you only had one album that's probably one that you might might take yeah um you said three three I don't know if it's a song anything by the Gravel dead and anything any Jerry Garcia jam that goes on for 25 minutes I'll take that
that's good value good yeah for for for money you got a choice the money little too sorry it goes on for a little time exactly I went to one Gravel dead comes up but it was post Jerry Garcia but but you don't remember it I didn't I don't remember very well yeah it was good it was good very good
well Marty thanks very much it's been a real pleasure having you on the podcast I appreciate it great thank you very much well I hope you enjoyed that as much as I did it was really a great conversation from my perspective um I want to thank you for staying with us and listening to
our thing and listening to the advert if you add any um just to remind you all the money from our advertising goes to the Monarch School for kids of family and homeless families um and uh I really appreciate the the fact that we've listened and you put the hours in I think it's
an incredible industry that we work in and so um but it's important to keep up with what's going on so thank you thanks to Aaron Hammock and Brooke Ellsworth for all the work they do on helping me get the podcast out and uh stay safe until next time
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